AlphaFold, the artificial intelligence tool developed by Google DeepMind to predict protein structure, marked a watershed moment in protein modeling and drug discovery. AlphaFold 2 is able to predict the three-dimensional structure of proteins with very high accuracy — close to that obtained experimentally, which takes months to years. The latest iteration, AlphaFold 3 (released in May), goes a step further, achieving greater accuracy than its predecessor, along with expanded functionality. AlphaFold 3 can predict the structure of not only proteins but also complexes that contain proteins and other structures, such as nucleic acids and small molecules. Indeed, the authors expect that modeling capabilities will continue to improve, due to advances in both deep learning and experimental approaches. In recognition of the importance of these advances, half of the Nobel Prize for chemistry has been awarded to John Jumper and Demis Hassabis, who led the development of AlphaFold (with the other half awarded to David Baker, University of Washington, for achievements in computational protein design).

Original references: Nature 630, 493–500 (2024); NobelPrize.org https://go.nature.com/4evrnLN (13 November 2024)

AlphaFold 是 Google DeepMind 开发的用于预测蛋白质结构的人工智能工具，标志着蛋白质建模和药物发现的分水岭。AlphaFold 2 能够以非常高的精度预测蛋白质的三维结构——接近通过实验获得的结果，这需要数月到数年的时间。最新版本 AlphaFold 3（于 5 月发布）更进一步，实现了比其前身更高的准确性，并扩展了功能。AlphaFold 3 不仅可以预测蛋白质的结构，还可以预测包含蛋白质和其他结构（如核酸和小分子）的复合物。事实上，由于深度学习和实验方法的进步，作者预计建模能力将继续提高。为了认识到这些进步的重要性，诺贝尔化学奖的一半被授予了领导 AlphaFold 开发的 John Jumper 和 Demis Hassabis（另一半被授予华盛顿大学的 David Baker，以表彰他在计算蛋白质设计方面的成就）。

原始参考资料：Nature630， 493–500 （2024）;NobelPrize.org https://go.nature.com/4evrnLN（2024 年 11 月 13 日）